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After reading this, is machine learning just the coded version of what you do in Calculus II? Analyzing a scatter plot and trying different equations to get the correlation coefficient value close to "1" and predict the next value? Because that would seem to me to have limited usefulness.

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What you describe sounds like regression, which is a powerful technique with varied applications, but is not the same as deep learning. Aspects from calculus do play an important role in ML notably in the gradient descent algorithm used to adjust weights. If you’re interested in learning more I recommend the 3blue1brown series on YouTube

I would say deep learning is a type of regression. The architecture of the network dictates the form of the equation. We're betting that equations of that form: 1) can fit the data 2) can generalize well to data that it wasn't fit against and 3) fitting/training is tractable. That allows making predictions about complicated data like pictures, words, sounds, etc. We may know the picture and need to predict a label for it.

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